Journal Article

Robust estimation of rice flag leaf inclination angle from SfM-MVS point clouds via ensemble skeleton extraction: validation in field and pot experiments

Pan, Yuanyuan, Zhang, Wenyu, Ye, Shenghao, Bi, Ran, Sun, Ting, Lu, Zhuoting, Wei, Menglei, Jiang, Min, Zhang, Jiayi, Li, Minglu, Zhou, Haoyang, Cao, Jing, Yang, Yue, Fang, Pingping, Hu, Jianan

2026

Overview

Background Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. Results In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R 2  = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. Conclusions This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: 3D reconstruction / Agricultural production / Agronomy / Angle / Biological Techniques / Biomedical and Life Sciences / Canopies / Chlorophyll / Crops / Cultivars / Efficiency / Experiments / Image processing / Inclination angle / Interception / Leaf inclination angle (LIA) / Leaves / Life Sciences / Light interception / Measurement techniques / Methods / Nitrogen / Phenotype / Phenotypes / Phenotypic plasticity / Photosynthesis / Physiological aspects / Physiology / Plant Sciences / Point cloud processing / Potassium / Remote sensing / Rice / Robustness / Senescence / Skeleton extraction / Three dimensional models